Computing environments, such as data centers, frequently employ cloud computing platforms, where “cloud” refers to a collective computing infrastructure that implements a cloud computing paradigm. For example, as per the National Institute of Standards and Technology, cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud-based data centers are deployed and managed by cloud service providers, who provide a computing environment for customers (tenants) to run their application programs (e.g. business applications or otherwise). Such cloud computing platforms may be implemented at least in part utilizing one or more virtual compute elements such as one or more virtual machines (VMs) or one or more containers. By way of example, one commonly used type of container is a Docker container.
In such a cloud computing platform, data may typically have to be moved across one or more networks. Reasons for such data movement include, but are not limited to, data migration into or out of the cloud environment, cross-site data protection, or re-scheduling of workflow instances.
Enterprises (e.g., companies, institutions, etc.) typically have their own “on-premises” computing platforms (as compared with an “off-premises” computing platform such as the above-described cloud computing platform or data center). Within the on-premises context, various data moving technologies have been developed and employed. These traditional enterprise-level data moving techniques are designed to be tightly coupled, efficient, and have rich features such as, e.g., data compression and data deduplication. Such enterprise-level techniques tend to have sufficient recovery time objective (RTO) and recovery point objective (RPO) metrics. However, enterprise-level data moving techniques may not always be adequate outside the on-premises context.